Course Modules

Module 1

Introduction to Machine Learning

What machine learning is, supervised vs unsupervised learning, the ML workflow, and your first model in Python.

Learn

Module 2

Preparing Data for Machine Learning

Clean data, handle missing values, encode categories, scale features and split into training and test sets.

Learn

Module 3

Linear Regression

Predict numbers such as house prices, understand the line of best fit, and measure error with MSE and R².

Learn

Module 4

Logistic Regression

Predict yes-or-no outcomes like customer churn with probabilities, the sigmoid curve and decision thresholds.

Learn

Module 5

Evaluating Models

Accuracy, precision, recall, F1, the confusion matrix, ROC curves and cross-validation, explained simply.

Learn

Module 6

K-Nearest Neighbours (KNN)

Classify by similarity, choose the best k, and tune a model with GridSearchCV.

Learn

Module 7

Decision Trees

How trees split data with Gini impurity, why they overfit, and how to read and prune them.

Learn

Module 8

Random Forest and Gradient Boosting

Ensemble methods that combine many trees into some of the most accurate models for table data.

Learn

Module 9

Support Vector Machines (SVM)

Margins, support vectors, the C parameter and kernels, with handwritten digit recognition.

Learn

Module 10

Naive Bayes

Probability-based classification, from spam filters to text classification with scikit-learn.

Learn

Module 11

K-Means Clustering

Find customer segments without labels, choose k with the elbow method and silhouette score.

Learn

Module 12

Dimensionality Reduction with PCA

Shrink many features into a few while keeping most of the information, and speed up models.

Learn

Module 13

Capstone Project: Predicting Customer Churn

An end-to-end machine learning project for a fictional broadband company, from messy data to business recommendations.

Learn

CX Learn Certification Exam

Earn all 13 module stars to unlock the final 30-question certification exam. Pass the exam to receive your official verified CadetX Machine Learning Certificate.

Requires 13 more stars

What you'll learn in this machine learning course

By the end of the course, you'll be able to take a real dataset and turn it into a working, well-evaluated machine learning model.

Explain supervised, unsupervised and reinforcement learning in simple terms

Clean and prepare real data for machine learning with pandas and scikit-learn

Build regression models to predict numbers like prices and sales

Build classification models to predict outcomes like churn and spam

Evaluate models properly with precision, recall, ROC curves and cross-validation

Use powerful ensemble models: random forests and gradient boosting

Find hidden groups in data with K-means clustering

Reduce complex data with PCA

Avoid common traps: overfitting, data leakage and the accuracy trap

Complete an end-to-end machine learning project for your portfolio

How the course works

Every module follows the same simple pattern, so you always know what comes next.

Read

Short lessons in plain English, with everyday analogies and clear diagrams.

Try

Interactive labs: drag a line of best fit, grow a random forest, or run K-means step by step, and see the results change instantly.

Code

Real Python and scikit-learn code for every model, with the real output explained line by line.

Check

A 5-question quiz at the end of every module, marked instantly.

Prove it

Finish the capstone project and pass the exam to earn your certificate.

Who this machine learning course is for

Students and graduates

Building skills for data science and AI roles, or adding a machine learning project to your CV.

Career changers

Moving into data from another field, with no maths degree needed. We explain the ideas, not the equations.

Data and BI analysts

Already comfortable with SQL, Excel or Python, and ready to move from reporting to prediction.

Curious beginners

Wanting to understand how the machine learning behind recommendations, spam filters and fraud detection really works.

Why learn machine learning?

Machine learning is behind the apps and services you use every day: the films Netflix suggests, the fraud checks on your bank card, the spam filter in your inbox and the price predictions on property sites. Employers in the UK, India and around the world are looking for people who can turn data into predictions, and machine learning skills are central to roles like data scientist, machine learning engineer, data analyst and AI engineer.

This course focuses on the practical machine learning skills employers ask for: preparing messy data, choosing the right algorithm, evaluating a model honestly and explaining results to non-technical people. You'll use scikit-learn, the most widely used machine learning library in Python, so everything you learn transfers directly to real projects and job interviews.

Once you've finished, you'll be ready for our Neural Networks and Deep Learning course, which covers CNNs, LSTMs, Transformers and transfer learning.